Automated Guided Vehicle, System Having A Computer And An Automated Guided Vehicle, And Method For Operating An Automated Guided Vehicle

ABSTRACT

The invention relates to an automated guided vehicle, a system having a computer and an automated guided vehicle, and a method for operating an automated guided vehicle. The automated guided vehicle is to travel along track sections automatically from a start point to an end point.

TECHNICAL FIELD

The invention relates to an automated guided vehicle, a system having acomputer and an automated guided vehicle, and a method for operating anautomated guided vehicle.

BACKGROUND

An automated guided vehicle (AGV) is a floor-bound means of conveyancehaving its own drive, which is automatically controlled and guidedwithout physical contact. Automated guided vehicles travel automaticallyfrom a start point to an end point.

SUMMARY

The objective of the invention is to create conditions enabling anautomated guided vehicle to travel more quickly from its start point toits end point.

The objective of the invention is attained by means of a method foroperating an automated guided vehicle that is configured to travelautomatically from a start point to an end point within an environment,wherein the environment comprises intermediate points, and tracksections connecting the intermediate points, the start point, and theend point, having the following steps:

-   -   a) Providing a graph assigned to the environment, comprising        nodes assigned to the intermediate points, a start node assigned        to the start point, an end node assigned to the end point, and        edges assigned to the corresponding track sections connecting        the start node, the end node and the nodes, wherein information        regarding the navigability is assigned to each edge, concerning        whether the corresponding track section can be travelled at a        specific point in time, and the travel time required by the        automated guided vehicle for travelling the corresponding track        section that can be navigated,    -   b) based on the information assigned to the edges and the        current point in time at the start point, calculating a strategy        for selecting the track sections along which the automated        guided vehicle can to travel automatically in order to arrive at        the end point in the quickest manner possible, wherein the        strategy comprises data regarding calculated potential arrival        times at the corresponding intermediate points and the end        point,    -   c) based on the strategy, automatic travelling of the automated        guided vehicle from the start point to the corresponding        intermediate point, adjacent to the start point, along the        corresponding track section,    -   d) based on the current arrival time at the intermediate point        adjacent to the start point, and based on the strategy, an        automatic travelling of the automated guided vehicle along the        corresponding track section to the next intermediate point along        the corresponding track section,    -   e) based on the current arrival time at the next intermediate        point, and based on the strategy, automatically travelling of        the automated guided vehicle to the intermediate point following        the next intermediate point, and    -   f) repeating steps d) and e) until the automated guided vehicle        arrives at the end point.

The automated guided vehicle can, for example, determine the desiredtrack sections, or intermediate points, respectively, on its own.Another aspect of the invention relates accordingly to an automatedguided vehicle, having a vehicle base body, numerous wheels, pivotallysupported in relation to the vehicle base body, for moving the automatedguided vehicle, at least one drive coupled to at least one of thewheels, for driving the corresponding wheel, and a control devicecoupled to the at least one drive, wherein the automated guided vehicleis configured to travel automatically from a start point to an end pointwithin an environment, controlled by its control unit, wherein theenvironment comprises intermediate points, and track sections connectingthe intermediate points, the start point and the end point, and thecontrol device is configured to calculate a strategy pursuant to themethod according to the invention, according to which the automatedguided vehicle travels automatically from the start point to the endpoint.

The desired track sections, or intermediate points, respectively, can bedetermined by an external computer, for example. Another aspect of theinvention therefore relates to a system, having:

-   -   An automated guided vehicle, having a vehicle base body,        numerous wheels pivotally mounted in relation to the vehicle        base body, for moving the automated guided vehicle, at least one        drive coupled to at least one of the wheels, for driving the        corresponding wheel, and a control device coupled to the at        least one drive, wherein the automated guided vehicle is        configured to travel automatically within an environment from a        start point to an end point, controlled by means of its control        device, wherein the environment comprises intermediate points        and track sections connecting the intermediate points, the start        point, and the end point, and    -   a computer, which is configured to calculate the strategy        pursuant to the method according to the invention, in order that        the automated guided vehicle travels automatically from the        start point to the end point, and which is configured to        transmit, in particular in a wireless manner, information        regarding the strategy to the automated guided vehicle.

The automated guided vehicle is, for example, a mobile robot. Theautomated guided vehicle designed as a mobile robot can comprise a robotarm, having numerous links disposed successively, which are connected bymeans of joints. The robot arm can, for example, be attached to thevehicle base body. The control device for moving the wheels can also beconfigured for moving the robot arm.

The automated guided vehicle can preferably be designed as a holonomic,or omnidirectional, automated guided vehicle. In this case, theautomated guided vehicle comprises omnidirectional wheels, preferablyso-called Mecanum wheels, which are controlled by the control device.

According to the invention, the automated guided vehicle should travelautomatically within the environment, e.g. a hall, from a start point toan end point. The environment comprises, aside from the start point andthe end point, the intermediate points, which are connected by means ofthe track sections. During the automatic travel, the automated guidedvehicle moves from the start point to the end point via numerousintermediate points and along the corresponding track sections.

According to the invention, the nodes of the graphs are assigned to theintermediate points of the environment, the start node is assigned tothe start point, and the end node is assigned to the end point. Theedges of the graph are assigned to the individual track sections, andcomprise, respectively, information regarding the temporal navigabilityof the individual track sections, thus, whether the corresponding tracksection can be travelled at a specific point in time. Furthermore, eachedge comprises information regarding the travel time required by theautomated guided vehicle for travelling the course of the correspondingnavigable track section. The strategy is calculated based on theseinformation. Subsequently, the automated guided vehicle can travelautomatically along the corresponding track sections from the startpoint to the end point.

In order to travel automatically from the start point to the end point,the strategy is first calculated, prior to the travel, based on whichthe automated guided vehicle travels automatically from the start pointto the end point. The strategy is based on the current point in time atthe start node, and takes into account the information assigned to theedges. In this manner, for various potential, or calculated, arrivaltimes at various intermediate points, and based on the actual arrivaltimes at the corresponding intermediate points, the next intermediatepoint can be determined automatically, and travelled to automatically,this intermediate point being the one that can be travelled to mostquickly by the automated guided vehicle at the current point in time.

According to one embodiment of the method according to the invention,the vehicle travels along the track sections, controlled, in particular,by means of a control device for the automated guided vehicle, based ona virtual map of the environment. The virtual map is, for example,stored in the control device for the automated guided vehicle, and mapsthe environment. By way of example, the automated guided vehicle can mapthe environment during the travel using sensors, and determine itsposition in the environment based on the virtual map.

According to a preferred embodiment of the method according to theinvention, during the automatic travel of the automated guided vehicle,an updated strategy is calculated at least one of the correspondingintermediate points at the current point in time, and based on theinformation assigned to the edges, regarding along which track sectionthe automated guided vehicle is able to travel automatically mostquickly from the current intermediate point to the end point, whereinthe updated strategy comprises information regarding calculatedpotential arrival times at the corresponding intermediate points and theend point. Following this, the automated guided vehicle travelsautomatically to the end point, based on the updated strategy. Theupdated strategy is preferably calculated by the automated guidedvehicle itself. The updated strategy, however, can also be calculated bya computer. For the calculation of the updated strategy, the currentnode is basically used as the new start node. According to theinvention, the edges comprise the information regarding thenavigability, thus, whether at a specific point in time, thecorresponding track section can be travelled, thus, in particular, atwhich points in time during the day the corresponding track section canbe travelled. Because time elapses during the travel of the automatedguided vehicle, it may be possible that during the travel, other tracksections allow for a shorter travel time to the end point than the tracksections assigned to the original strategy. Because of the determinationof the updated strategy during the travel, it is possible, according tothis embodiment, to obtain a shorter travel time for the automatedguided vehicle in some cases.

The navigability of the track sections and/or the information regardingthe corresponding travel times can, for example, be determinedempirically, and/or in the framework of a learning phase. In particular,the navigability of the corresponding track section and/or theinformation regarding the corresponding travel times can beautomatically updated during the automatic travel of the automatedguided vehicle.

According to a preferred embodiment on the method according to theinvention, the respective information, regarding whether thecorresponding track section is navigable at a specific point in time, isprovided as a stochastic process describing the navigability of thecorresponding track section at specific points in time. The informationregarding the corresponding travel times are preferably, in each case,provided as a probability distribution, which is modeled on theestimated travel times required by the automated guided vehicle fornavigable track sections. By means of this probability distribution, orstochastic process, respectively, the shortest travel time from thestart point to the end point can be mathematically calculated, orestimated, in a relatively simple manner.

Preferably, the travel times that are to be expected for the individualtrack sections can be calculated, these being calculated, as probabilitydistributions for the arrival times P(Y₁) at the next intermediatepoint, according to the following formula:

${P\left( Y_{1} \right)} = {\sum\limits_{x \in {\{{0,1}\}}}^{\;}{{P\left( {X_{t} = x} \right)}{P\left( {{Y_{t}X_{t}} = x} \right)}}}$

Where P(X_(t)) is the probability distribution for the navigability ofthe track section assigned to the corresponding edge, and P(Y_(t)|X_(t))is the conditional probability for the arrival time for the tracksection assigned to the corresponding edge, as a function of thenavigability.

BRIEF DESCRIPTION OF THE DRAWINGS

An exemplary embodiment of the invention is illustrated by way ofexample in the attached schematic figures.

FIG. 1 is a top view of an automated guided vehicle,

FIG. 2 is a side view of the automated guided vehicle of FIG. 1,

FIG. 3 is an environment for the automated guided vehicle,

FIG. 4 is a virtual map and a graph, and

FIG. 5 is the graph depicted in FIG. 4.

DETAILED DESCRIPTION

FIG. 1 shows, in a top view, schematically, an automated guided vehicle1, and FIG. 2 shows a side view of the automated guided vehicle 1.

The automated guided vehicle 1 is preferably designed such that it canmove freely in all directions. The automated guided vehicle 1 is, inparticular, designed as an omnidirectional moveable, or holonomic,respectively, automated guided vehicle. The automated guided vehicle 1can be a mobile robot, comprising a robot arm 21, having numerous links22, disposed successively, which are connected by means of joints 23.The robot arm 21 comprises, in particular, an attachment device, e.g. inthe form of a flange 24, to which an end effector, not shown, can beattached.

In the case of the present embodiment example, the automated guidedvehicle 1 has a vehicle base body 2, and numerous omnidirectional wheels3, which can also be referred to as Mecanum wheels. Wheels of this typecomprise, for example, a pivotably mounted rim, on which numerous rollerelements are supported, without a drive. The rims can be driven by meansof a drive. In the case of the present embodiment example, the wheels 3are each driven by means of an electric drive 4. These are preferablyfeedback controlled electric drives.

The automated guided vehicle 1 also has a control device 5 disposed onthe vehicle base body 2, which is connected to the drives 4. Ifapplicable, this can also control the movement of the robot arm 21, ifpresent.

The automated guided vehicle 1 is supposed to move automatically in anenvironment U shown in FIG. 3, in particular, to travel automaticallyfrom a start point SP to an end point ZP. For this, a computer programruns on the control device 5, which controls the drives 4 in such amanner that these automatically move the automated guided vehicle 1 fromthe start point SP to the end point ZP.

In the case of the present embodiment example, not only the start pointSP and the end point ZP are assigned to the environment U, but also,numerous intermediate points are assigned thereto. In the case of thepresent embodiment example, the intermediate points comprise a firstintermediate point 31, a second intermediate point 32, a thirdintermediate point 33, a fourth intermediate point 34, and a fifthintermediate point 35.

The intermediate points 31-35 are connected by means of track sections,on which the automated guided vehicle 1, if applicable, can travelbetween two intermediate points. FIG. 3 shows a first track section 41,which connects the start point SP to the first intermediate point 31, asecond track section 42, which connects the first intermediate point 31to the end point ZP, a third track section 43, which connects the firstintermediate point 31 to the second intermediate point 32, a fourthtrack section 44, which connects the second intermediate point 32 to thethird intermediate point 33, a fifth track section 45, which connectsthe third intermediate point 33 to the end point ZP, a sixth tracksection 46, which connects the end point ZP to the fourth intermediatepoint 34, and a seventh track section 47, which connects the start pointSP to the fifth intermediate point 35.

The automated guided vehicle 1 further comprises at least one sensor 6,connected to the control device 5, and, e.g., disposed on the vehiclebase body 2. The sensor 6, or sensors, respectively, comprise, e.g., atleast one laser scanner and/or at least one camera, and are provided forrecording, or scanning, respectively, the environment U of the automatedguided vehicle 1 during its automatic travel, or creating images of theenvironment U of the automated guided vehicle 1, respectively. Thecontrol device 5, in turn, is configured to process, or evaluate,respectively, the signals or data arriving from the sensors 6, e.g., bymeans of image data processing. The at least one sensor 6 comprises,e.g. a 2D laser scanner, a 3D laser scanner, an RGB-D camera, and/or aTOF camera. TOF cameras are 3D camera systems, which measure distanceswith the time-of-flight method.

As has already been explained, the automated guided vehicle 1 isconfigured, in the case of the present embodiment example, to travelautomatically from the start point SP to the end point ZP within theenvironment U. For this, in the case of the present embodiment example,e.g., a virtual map 40, shown in FIG. 4, or a digital map, respectively,of the respective environment U in which the automated guided vehicle 1is to move, is stored in the control device 5. The environment U is,e.g. a hall. The virtual map 40 is created, e.g. by means of a so-calledSLAM method, e.g. based on signals, or data, respectively, from thesensors 6 and/or based on wheel sensors, not shown in detail, assignedto the wheels 3. The virtual map 50 is, e.g. stored in a memory 7 of theautomated guided vehicle 1, which is coupled to the control device 5.The virtual map 40 can, e.g. be depicted by means of a display device 8.By this means, it is possible for the automated guided vehicle to moveautomatically to the end point ZP, as soon as it is known to it, alongwhich track sections of the environment U it is to travel automaticallyto the end point ZP.

In the case of the present embodiment example, the track sections thatare to be travelled are determined using a graph G shown in FIGS. 4 and5. In particular, the track sections are determined based on apotentially changing environment U, which can change in that, amongother things, at least one of the track sections is not alwaysnavigable, and in that the time required by the automated guided vehicle1 for travelling the course of a specific track section in theenvironment U can change. This planning is executed, e.g., by theautomated guided vehicle 1 itself, e.g. by means of its control device5, or a computer program, respectively, running on its control device 5.The planning can, however, e.g. also be executed by means of an externalcomputer 10, or a computer program, respectively, running on thecomputer 10, wherein the computer 10 preferably transmits the results ofthe planning to the control computer 5 in a wireless manner. The graph Gcan, e.g., be depicted together with the virtual map 40 on the displaydevice 8, as is illustrated in FIG. 4.

The graph G comprises numerous nodes, and edges connecting the nodes.The graph G comprises, in particular, a start node s, an end node z, afirst node k1, a second node k2, a third node k3, a fourth node k4, anda fifth node k5. The graph G comprises, in particular, a first edge 51,a second edge 52, a third edge 53, a fourth edge 54, a fifth edge 55, asixth edge 56, and a seventh edge 57. In the case of the presentembodiment example, the first edge 51 connects the start node s to thefirst node k1, the second pathway 52 connects the first node k1 to theend node z, the third edge 53 connects the first node k1 to the secondnode k2, the fourth edge 54 connects the second node k2 to the thirdnode k3, the fifth edge 55 connects the third node k3 to the end node z,the sixth edge 56 connects the end nod z to the fourth node k4, and theseventh edge 57 connects the start node s to the fifth node k5.

In the case of the present embodiment example, the start point SP in theenvironment U is assigned to the start node s on the graph G, and theend point ZP is assigned to the end node z. Furthermore, theintermediate points 31-35 in the environment U are assigned to the nodesk1-k5 of the graph G, and the track sections 41-47 are assigned to theedges 51-57. In particular, the first intermediate point 31 in theenvironment U is assigned to the first node k1 of the graph G, thesecond intermediate point 32 in the environment U is assigned to thesecond node k2 of the graph G, the third intermediate point 33 in theenvironment U is assigned to the third node k3 of the graph G, thefourth intermediate point 34 in the environment U is assigned to thefourth node k4 of the graph G, and the fifth intermediate point 35 inthe environment U is assigned to the fifth node k5 of the graph G. Inparticular, the first track section 41 is assigned to the first edge 51,the second track section 42 is assigned to the second edge 52, the thirdtrack section 43 is assigned to the third edge 53, the fourth tracksection 44 is assigned to the fourth edge 54, the fifth track section 45is assigned to the fifth edge 55, the sixth track section 46 is assignedto the sixth edge 56, and the seventh track section 47 is assigned tothe seventh edge 57.

The graph G is, in particular, a directed graph G, for which reason theedges 51-57 are depicted as arrows in FIGS. 4 and 5. The graph G can bedescribed mathematically by means of the following equation:

G=<V,E>

where V is a finite quantity of nodes s, z, k1-k5, and E⊂V×V is the setof edges 51-57.

In the case of the present embodiment example, each individual edge41-47 is assigned information regarding the track section 51-57 to whichit is assigned.

In the case of the present embodiment example, the information regardingthe corresponding track section 51-57 comprises a navigability of thecorresponding edge 51-57, or the corresponding track section 41-47,respectively, thus information regarding whether the automated guidedvehicle 1 can travel on the corresponding track section at a specificpoint in time, and the travel time required by the automated guidedvehicle 1 to travel the corresponding navigable track section.

In the case of the present embodiment example, a stochastic process isassigned to each of the edges 51-57, which describes the navigability ofthe corresponding track section 41-47 at specific points in time, and aprobability distribution, modeling the probable travel time required bythe automated guided vehicle 1 for navigable track sections 41-47.

The planning of the track sections that are to be travelled is executedby means of the graph G. In the framework of the planning, a strategy iscalculated, regarding along which track sections 41-47 the automatedguided vehicle 1 is able to automatically travel to the end point ZPmost quickly. The strategy comprises information regarding calculatedpotential arrival times at the corresponding intermediate points 31-35,and the end point ZP. The planning can be repeated during the travel ofthe automated guided vehicle 1 to at least one of the intermediatepoints 31-35, through which the automated guided vehicle 1 passes duringits travel, wherein this intermediate point then represents a new startpoint for the updated planning. When the planning is repeated, anupdated strategy is calculated, according to which the automated guidedvehicle 1 travels automatically from the current intermediate point tothe end point ZP.

The planning of the track sections that are to be travelled canpreferably viewed as a Markov decision process, in which

S={(i,t)|iεV,tεN ₀}

is the state space assigned to the start point SP, the intermediatepoints 31-35, and the end point ZP, or, respectively, the start node s,the end node z and the intermediate nodes k1-k5, and the arrival time atthe corresponding intermediate points 31-35, or the end point ZP,respectively.

A(i, t)=N_(G)(i) is the activity space that is assigned in the graph Gfor each state (i, t)εS, wherein N_(G)(i) is the set of all neighboringnodes i, i.e. the nodes k1-k5, the start node s and the end node z.

P((j, t′)|(l, j), j) is a navigability model, or a navigabilityprobability, to which a probability is assigned for the arrival of theautomated guided vehicle 1 at the j-th intermediate point at time t′, ifit has started travelling from the i-th intermediate point at time t.This probability is a function of the navigability of the track sections41-47 assigned to the corresponding edges (i, j) (edges 51-57), and theprobability distribution, which models the probable travel time requiredfor the automated guided vehicle 1 on the navigable track section. Thepresent Markov decision process is, in particular, a so-calledleft-right model, because none of the transitions in states, theassigned arrival times of which can be less than or equal to the currentstate, are possible.

An average cost function is indicated by c: S×S→N₀∪{∞}, to which thetime required by the automated guided vehicle 1 to travel from one stateto another, thus from one intermediate point to the next, is assigned.If the corresponding navigability probability is zero, then the costfunction is set at “infinite.”

With the given end node z (zεG), an optimal strategy π* can becalculated, which minimizes the expected arrival time at the end pointZP for each state s. The minimum expected arrival time C*(i, t) for eachstate (i, t)εS can be calculated with the following equation (equationno. 1):

${C*\left( {i,t} \right)} = {\min\limits_{j \in {A{({i,t})}}}{\sum\limits_{t^{\prime} = {t + 1}}^{\infty}{C*\left( {j,t^{\prime}} \right){P\left( {{\left( {j,t^{\prime}} \right)\left( {i,t} \right)},j} \right)}}}}$

The target state (z, t) assigned to the end node z is initialized, inparticular, with t, i.e. C*(z, t)=t.

In order to obtain a relatively efficient calculation of the arrivaltime, in particular, preferably by means of a dynamic calculation, inthe case of the present embodiment example, the fact that a point intime t_(m) exists is exploited, such that C*(i,t_(m)+t)=C*(i,t_(m))+tfor all tεN₀. This means that for all time steps greater than t_(m), thetime required for reaching the end point ZP is not dependent on thecurrent point in time.

X_(t) is a random variable, describing the navigability of a specificedge 51-57, or its assigned track section 41-47 at the point in time t,wherein

$X_{t} = \left\{ \begin{matrix}{0,} & {{if}\mspace{14mu} {the}\mspace{14mu} {corresponding}\mspace{14mu} {pathway}\mspace{14mu} {at}\mspace{14mu} {point}\mspace{14mu} {in}\mspace{14mu} {time}\mspace{14mu} t\mspace{14mu} {is}\mspace{14mu} {navigable}} \\{1,} & {{if}\mspace{14mu} {the}\mspace{14mu} {corresponding}\mspace{14mu} {pathway}\mspace{14mu} {at}\mspace{14mu} {point}\mspace{14mu} {in}\mspace{14mu} {time}\mspace{14mu} t{\mspace{11mu} \;}{is}\mspace{14mu} {not}\mspace{14mu} {navigable}}\end{matrix} \right.$

In the case of the present embodiment example, this probability for theindividual points in time is determined empirically. It is, however,also possible for this probability to be determined automatically, bymeans of the automated guided vehicle 1, in that, e.g., during alearning phase, it automatically travels through the environment, andstores information thereby for the individual points in time regardingwhen which track section is navigable. This learning phase can alsooccur, at least in part, during the running operation, in order, e.g.,to improve the information regarding said probability. The learningphase can occur in its entirety during the running operation.

In the case of the present embodiment example, it is assumed that thenavigability probability for a specific edge, or its assigned tracksection, respectively, is independent of the navigability probability ofthe remaining edges, or their assigned track sections. Furthermore, inthe case of the present embodiment example, a homogenous Markov processof the first degree, having a navigability matrix

$A = \begin{bmatrix}P_{00} & P_{01} \\P_{10} & P_{11}\end{bmatrix}$

is assumed.

If t approaches “infinity,” X_(t) approaches a stationary distribution.The time required for this approaching is referred to as a combinedtime. The combined time for the entire graph G is:

t _(m)=max{t _(m) ^(e) |eεE}, wherein t _(m) ^(e)

is the combined time for the respective edges 51-57.

In the case of the present embodiment example, the expected travel timesthrough corresponding track sections 41-47, assigned to the edges 51-57,are modeled as a stochastic process Y_(t), which expresses theprobability that the automated guided vehicle 1 will reach the nextintermediate point at a specific point in time. In particular,P(Y_(t)=t′) is the probability that the automated guided vehicle 1 willarrive at the j-th intermediate point at the point in time t′, when itleaves the i-th intermediate point (which can also be the start pointSP) at the point in time t. The probability distribution for the arrivaltime P(Y_(t)) can be calculated as follows:

${P\left( Y_{t} \right)} = {\sum\limits_{x \in {\{{0,1}\}}}^{\;}{{P\left( {X_{t} = x} \right)}{P\left( {{Y_{t}X_{t}} = x} \right)}}}$

wherein P(X_(t)) is the probability distribution for the navigability ofthe track section assigned to the corresponding edge, and P(Y_(t)|X_(t))is the conditional probability for the arrival time of the track sectionassigned to the corresponding edge, as a function of the navigability.In the case of the present embodiment example, Y_(t)−t is the traveltime assigned to the track section.

The travel time is time dependent, if the corresponding track section isnavigable. In the case of the present embodiment example, the so-calleddiscrete approaching of the beta distribution can be used to calculatethe conditional arrival time, i.e.

P(Y _(t) |X _(t)=0)˜Beta(α,β;a+t,b+1).

If a track section cannot be travelled on, then for the present exampleembodiment, the distribution of the arrival time is given as:

${P\left( {{Y_{t}X_{t}} = 1} \right)} = {{\sum\limits_{n = 0}^{\infty}{\left( P_{11} \right)^{n}P_{10}{P\left( {Y_{t + n + 1}X_{t}} \right)}}} = 0}$

In the case of the present embodiment example, it is assumed that thestate of an edge, or its track section, respectively, must change beforethe intermediate point assigned to the next node can be reached. Theexpected arrival time assigned to a pathway at the point in time t isobtained by

${E\left\lbrack Y_{t} \right\rbrack} = {{E\left\lbrack {{Y_{t}X_{t}} = 0} \right\rbrack} + \frac{P\left( {X_{t} = 1} \right)}{1 - p_{11}}}$

wherein E[Y_(t)|X_(t)=0] is the expected arrival time assigned to anedge, if this edge is navigable, and

$\frac{P\left( {X_{t} = 1} \right)}{1 - p_{11}}$

is the expected time step, or elapsed time, respectively, during whichthe automated guided vehicle 1 must presumably wait at a node, orintermediate point, respectively, until the subsequent edge, or thesubsequent track section is again navigable.

Thus, the arrival time probability distribution assigned to the pathwayspecific edge is expressed as

P((j,t′)|(i,j),j)=P(Y _(t) ′=t′).

In order to obtain the minimum expected arrival time C*(i, t) for eachof the states (i, t)εS, in the case of the present embodiment example,the minimum expected arrival time C*(i, t_(m)) is calculated for eachstate at the point in time t_(m), by means of the following formula:

${C*\left( {i,t_{m}} \right)} = {\min\limits_{j \in {A{({i,t})}}}{\sum\limits_{t^{\prime} = {t + 1}}^{\infty}{\left( {{C*\left( {j,t_{m}} \right)} + t} \right){{P\left( {Y_{t_{m}} = 1} \right)}.}}}}$

This equation is obtained through the use of equation no. 1. Throughconversion, the following is obtained:

${C*\left( {i,t_{m}} \right)} = {{\min\limits_{j \in {A{({i,t})}}}{C*\left( {j,t_{m}} \right)}} + {{E\left\lbrack Y_{t_{m}} \right\rbrack}.}}$

As already described above, for the end point ZP, thus for the end nodez, the following is obtained: C*(j, t_(m))=t_(m). By way of example, bymeans of Dijkstra's algorithm, known in principle to the person skilledin the art, the expected arrival time at the end point ZP can becalculated for all intermediate nodes k1-k5 at the point in time t_(m),in that, in the case of the present embodiment example, the expectedtravel time E[Y_(t) _(m) ]−t_(m) at the point in time t_(m) can be usedfor the cost function for each edge 51-57.

Assuming that the expected arrival time at the point in time t=t_(m) hasalready been calculated, the expected arrival times for t<t_(m) can becalculated according to the equation no. 1, e.g. by means of iteration,starting with the point in time t_(m)−1.

In particular, for each state (i, t) in S, only those expected arrivaltimes for the time steps greater than t are needed, which have eitheralready been calculated, or can be calculated according to C*(i,t_(m+1))=C*(i, t_(m))+t.

The value for the equation no. 1 can, e.g., be calculated according tothe following equation:

$\begin{matrix}{{C*\left( {i,t} \right)} = {A + B}} \\{= {{\min\limits_{j \in {A{({i,t})}}}{\sum\limits_{t^{\prime} = {t + 1}}^{T}{C*\left( {j,t^{\prime}} \right){P\left( {Y_{t} = t^{\prime}} \right)}}}} + {\sum\limits_{t^{\prime} = {T + 1}}^{\infty}{C*\left( {j,t^{\prime}} \right){P\left( {Y_{t} = t^{\prime}} \right)}}}}}\end{matrix}$

wherein the time step T is the maximum between t_(m) and t*+1, whereint* is the greatest value with regard to P(Y_(t)|X_(t)=0).

For the summands A, the minimum expected arrival times have already beencalculated. The summand B can be calculated by means of the followingequation:

$B = {{P\left( {X_{t} = 1} \right)}{P\left( {Y_{t} = {{{t^{*} + 1}X_{t}} = 1}} \right)}{\frac{p_{11}^{T - t^{*}}}{1 - p_{11}} \cdot {\left( {{C^{*}\left( {j,t_{m}} \right)} + T + 1 + t - t_{m} + \frac{p_{11}}{1 - p_{11}}} \right).}}}$

What is claimed is:
 1. A method, for operating an automated guidedvehicle within an environment, which is configured to travelautomatically from a start point to an end point, wherein theenvironment comprises intermediate points and track sections connectingthe intermediate points, the start point, and the end point, the methodcomprising: a) providing a graph assigned to the environment, the graphcomprising nodes assigned to the intermediate points, a start nodeassigned to the start point, an end node assigned to the end point, andedges assigned to the track sections connecting the start node, the endnode, and the nodes, wherein information is assigned to the edges ineach case, regarding the navigability of whether the corresponding tracksection can be travelled at a specific point in time and the travel timerequired by the automated guided vehicle for travelling the course ofthe corresponding navigable track section, b) based on the informationassigned to the edges and the current point in time at the start point,calculating a strategy for determining the track sections along whichthe automated guided vehicle can travel most quickly in an automatedmanner to the end point, wherein the strategy comprises informationregarding calculated potential arrival times at the correspondingintermediate points and the end point, c) based on the strategy,automatically moving the automated guided vehicle from the start pointto the corresponding intermediate point adjacent to the start point,along the corresponding track section, d) based on the current arrivaltime at the intermediate point adjacent to the start point, and based onthe strategy, automatically moving the automated guided vehicle to thenext intermediate point along the corresponding track section, e) basedon the current arrival time at the next intermediate point, and based onthe strategy, automatically moving the automated guided vehicle alongthe corresponding track section to the intermediate point, following thenext intermediate point, and f) repeating steps d) and e) until theautomated guided vehicle arrives at the end point.
 2. The method ofclaim 1, further comprising, during the automatic movement of theautomated guided vehicle along the determined track sections,calculating at least one updated strategy at one of the correspondingintermediate points at the current point in time, and based on theinformation assigned to the edges, determining the track sections alongwhich the automated guided vehicle can most quickly travel from thecurrent intermediate point to the end point, wherein the updatedstrategy comprises information regarding calculated potential arrivaltimes at the corresponding intermediate points and the end point, andautomatically moving the automated guided vehicle to the end point,based on the updated strategy.
 3. The method of claim 1, in which thenavigability of the corresponding track section and/or the informationregarding the corresponding travel time are determined empiricallyand/or in the framework of a learning phase.
 4. The method of claim 3,in which the navigability of the corresponding track section and/or theinformation regarding the corresponding travel times is automaticallyupdated during the automatic movement of the automated guided vehicle.5. The method of claim 1, wherein automatically moving the automatedguided vehicle comprises controlling the automated guided vehicle tomove along the track sections based on a virtual map of the environment.6. The method of claim 5, wherein controlling the automated guidedvehicle to move along the track sections is carried out by a controldevice for the automated guided vehicle.
 7. The method of claim 1, inwhich the respective information regarding whether the correspondingtrack section is navigable at a specific point in time, is implementedas a stochastic process describing the navigability of the correspondingtrack sections at specific points in time, and/or the informationregarding the corresponding travel times are each implemented as aprobability distribution, which provides a model of the expected traveltime required by the automated guided vehicle on navigable tracksections.
 8. The method of claim 1, in which the expected travel timesare calculated for the individual track sections, which are calculatedas probability distributions for the arrival times P(Y_(t)) at the nextintermediate point according to the following formula:${P\left( Y_{t} \right)} = {\sum\limits_{x \in {\{{0,1}\}}}^{\;}{{P\left( {X_{t} = x} \right)}{P\left( {{Y_{t}X_{t}} = x} \right)}}}$wherein P(X_(t)) is the probability distribution for the navigability ofthe track section assigned to the corresponding edge, and P(Y_(t)|X_(t))is the conditional probability for the arrival time of the track sectionassigned to the corresponding edge, as a function of the navigability.9. An automated guided vehicle, comprising: a vehicle base body, aplurality of wheels pivotally supported in relation to the vehicle basebody for moving the automated guided vehicle, at least one drive coupledto at least one of the wheels for driving the corresponding wheel, and acontrol device coupled to the at least one drive, wherein the automatedguided vehicle is configured to travel automatically, controlled bymeans of its control device, within an environment, from a start pointto an end point, and wherein the environment comprises intermediatepoints and track sections connecting the intermediate points, the startpoint, and the end point, and the control device is configured tocalculate a strategy pursuant to the method according to claim 1,according to which the automated guided vehicle travels automaticallyfrom the start point to the end point.
 10. A system, comprising: anautomated guided vehicle, having a vehicle base body, numerous wheels,pivotally supported in relation to the vehicle base body, for moving theautomated guided vehicle, at least one drive coupled to at least one ofthe wheels, for driving the corresponding wheel, and a control devicecoupled to the at least one drive, wherein the automated guided vehicleis configured to travel, controlled by its control device,automatically, within an environment, from a start point to an endpoint, wherein the environment comprises intermediate points and tracksections connecting the intermediate points, the start point and the endpoint, and a computer, which is configured to determine a strategypursuant to the method according to claim 1, according to which theautomated guided vehicle travels automatically from the start point tothe end point, and which is configured, in particular, to wirelesslytransmit information regarding the strategy to the automated guidedvehicle.